Spatial distribution of metabolites in the retina and its relevance to studies of metabolic retinal disorders

Introduction The primate retina has evolved regional specialisations for specific visual functions. The macula is specialised towards high acuity vision and is an area that contains an increased density of cone photoreceptors and signal processing neurons. Different regions in the retina display unique susceptibility to pathology, with many retinal diseases primarily affecting the macula. Objectives To better understand the properties of different retinal areas we studied the differential distribution of metabolites across the retina. Methods We conducted an untargeted metabolomics analysis on full-thickness punches from three different regions (macula, temporal peri-macula and periphery) of healthy primate retina. Results Nearly half of all metabolites identified showed differential abundance in at least one comparison between the three regions. Furthermore, mapping metabolomics results from macula-specific eye diseases onto our region-specific metabolite distributions revealed differential abundance defining systemic metabolic dysregulations that were region specific. Conclusions The unique metabolic phenotype of different retinal regions is likely due to the differential distribution of different cell types in these regions reflecting the specific metabolic requirements of each cell type. Our results may help to better understand the pathobiology of retinal diseases with region specificity. Supplementary Information The online version contains supplementary material available at 10.1007/s11306-022-01969-6.


Statistical Analysis -Human Serum AMD vs Controls
Metabolic differential abundance was tested using the same approach as described for the primate study. In model contained in age, sex at birth and processing batch as covariates. No intra-subject correlation was taken into account since only one observation was included per subject for each metabolite. To achieve maximal discovery power, subject-specific weights were calculated using the function arrayWeights from the Limma package and were included in the models. We performed two main analyses, one testing for all AMD patients against controls and another one testing each sub disease again controls. Enrichment analysis was performed for both main analyses as previously described and no principal component analysis was performed for this study.

Metabolite missingness in primate data
Given the relatively small number of detected metabolites and their strong correlations with each other, in this study, we used a somewhat forgiving missingness threshold to discard apriority any metabolite (>80%). However, we found that only 44 significant metabolites had an initial missingness rate greater than 20% and only 23 greater than 50% (Table S3). We tested whether significance was higher for those metabolites presenting different missingness rates between areas as a quality check. We found that significant metabolites had an average standard deviation of missingness across areas lower than those non-significant (AveMissSD 0.055 vs 0.068) which confirmed that our results were not biased towards metabolites presenting different missingness rates across areas.

Metabolite differential abundance AMD vs Controls
We found a total of 4 metabolites whose abundance was significantly different between AMD cases and controls. All these were depleted and only significant when comparing CNV-AMD to controls and only 1 of these was significant when comparing all AMD cases against controls (Table S3, Figure S12).

Pathway differential abundance AMD vs Controls
We then investigated whether metabolites belonging to the same metabolic pathway shared a similar abundance difference across retinal areas. We found two biological pathways to be enriched with metabolites sharing similar signals for disease status. Both were only significant when comparing CNV-AMD to controls and only one of these was significant when comparing all AMD cases against controls (Table S3, Figure S13).  In this image, every boxplot represents the covariate-corrected distribution of one metabolite whose abundance was significantly different for any of the three contrasts. Metabolites are divided into biochemical families which are sorted according to greater family-wise abundance in the macula (top) or in the periphery (bottom).

Figure S3
: Log-fold changes and 95% confidence interval of metabolites with significant differential abundance between macula and periphery. Positive log-fold changes values in this figure indicate that the metabolite abundance was higher in the macula while negative represent higher abundance in the periphery. Metabolites have been divided and coloured by their respective biological pathways. Figure S4: Log-fold changes and 95% confidence interval of metabolites with significant differential abundance between macula and termporal. Positive log-fold changes values in this figure indicate that the metabolite abundance was higher in the macula while negative represent higher abundance in the periphery. Metabolites have been divided and coloured by their respective biological pathways. Figure S5: Log-fold changes and 95% confidence interval of metabolites with significant differential abundance between temporal and periphery. Positive log-fold changes values in this figure indicate that the metabolite abundance was higher in the macula while negative represent higher abundance in the periphery. Metabolites have been divided and coloured by their respective biological pathways. Figure S6: Metabolic distribution between clusters. In this image, every boxplot represents the covariate-corrected distribution of one metabolite whose abundance was significantly different for any of the three contrasts. Metabolites are divided into clusters according to their combination of significant results and respective log-fold change direction, further defined in Table 1 (summary  statistics provided in Table S5). Boxplots have been coloured by the biological pathway of each metabolite. Increasing here is used to represent an increment of metabolic abundance from the macula to the periphery while decreasing represents the opposite. Figure S7: Pathway first PC distribution between retinal areas and divided by cluster. In this image, every boxplot represents the covariate-corrected distribution of the first PC for one pathway where this was significantly different for any of the three contrasts. Pathways are divided according to their combination of significant results and respective log-fold change direction (Table S5). Figure S8: Relative abundance of N metabolites (filled rectangles, or 'bricks') grouped by biochemical family/pathway (y axis) and ordered by greater family-wise abundance in females (left) and older (right). The number of bricks indicates the number of metabolites in the pathway available for this study. The colour of each brick represents the log-fold change a particular comparison (female vs males on the right and older vs younger left). Positive log-fold change (reds) indicates that the metabolite is more abundant while negative values (blues) indicate that the metabolite is depleted. + denotes family is significantly enriched (mainly red); -denotes family is significantly depleted (mainly blue); * denotes family is more differentially abundant in both directions than would be expected by chance ('mixed directionality'/signal mixture effect). Figure S9: Positive control plot showing differences in (un-non-normalise, unnon-imputed) and log-transformed values of two carotenoids (most likely lutein and zeaxanthin) measured across samples collected across different retinal locations. A) Carotene diol 1 B) Carotene diol 3. Note the missing boxplot for carotene diol 3 in the peripheral area is due to complete missingness (i.e., below limit of machine detection) of this metabolite (i.e., below limit of machine detection) inobserved across peripheral samples. Figure S11: First two principal components plot on AMD metabolic data identifying 3 outliers. This plot every sample was colored accondying the the disease status and the string represented the sample preparation sub-bacth. Figure S12: Log-fold changes and 95% confidence interval of metabolites with significant (FDR<0.05) differential abundance between CNV-AMD and healthy controls. Negative log-fold changes values in this figure indicate that the metabolite abundance was lower in CNV-AMD samples compared to controls. Metabolites have been divided and coloured by their respective biological pathways. Figure S13: Pathways enrichment results. In this figure, each row represents a biological pathway. Metabolites are presented as "bricks" in each row. The colour of each metabolite represents the log-fold changes of that metabolite between CNV-AMD and healthy controls. High log-fold change indicates that the metabolite is more abundant in the CNV-AMD while negative values indicated that the metabolite is depleted. The symbol '*' next to the pathway name indicates that that pathway was significantly enriched with either abundant or depleted metabolites in CNV-AMD compared to healthy controls.  Table S1 is provided as well as an enrichment analysis only version).  Table S3 is provided). Table S4: Table presenting the list of discarded metabolites. For each metabolite, a reason is presented for discarding it. Table S5: Retained metabolites used in this study. This list contains all metabolites as well their classification in metabolic pathways as super-pathways. Table S6: Table presenting metabolic clustering decision rules. For each cluster, the combination of significant effects for each contrast and their direction is presented.